Lead Data Architect (Investment Management) (Contract)

MAS Monetary Authority Singapore; Singapore; On-site; SGD 240,000 - 360,000; Full time

Job summary

MAS Monetary Authority Singapore is seeking a senior, hands-on Data Architect to own the data design of our investment management platforms. You will define how investment data is sourced, ingested, structured and governed across the front, middle and back office.

You will deliver implementation-ready artefacts and drive three unified data layers—portfolio holdings, structured market data, and unstructured content—with reconciliation and retrieval infrastructure.

Qualifications

• Degree in Computer Science/Information Systems/Engineering or related field
• 10+ years in data architecture or senior data engineering in financial services
• Experience with investment data domains and reconciliations
• Hands-on with vector search and retrieval technologies (embedding, indexing)

Responsibilities

• Design and govern end-to-end data architecture for investment platforms
• Define source-to-target data flows and data quality rules
• Lead data modelling across security, positions, transactions, pricing and valuation
• Define governance, metadata and lineage frameworks across three data layers
• Collaborate with AI engineers on retrieval and hybrid search integration

Skills

Data architecture

Data modelling

Investment management

Communication

Architecture reviews

Education

Degree in Computer Science / IS / Engineering

Tools

Snowflake

Databricks

Spark

Kafka

AWS

Azure

Docker

Kubernetes

Job description

[What the role is]

We are looking for a senior, hands‑on Data Architect to own the data design of our investment management platforms. You will define how investment data is sourced and ingested from multiple systems, structured and integrated within the organisation, governed and made available across front, middle and back office. You will stay close to implementation by producing models and specifications, reviewing build quality, and providing technical leadership to the team’s data specialists. The role must deliver solutions that meet MAS technology standards and applicable Whole of Government compliance requirements. The role sits in a four‑person delivery team set up to establish a unified investment data architecture that enables generative AI use cases. You will design and drive implementation of three unified data layers — portfolio holdings, structured market and economic data, and unstructured research, news and sell‑side content — together with the reconciliation and retrieval infrastructure that supports specific use cases across the investment value chain. You will join at the start of the build phase, designing against a product vision and user stories.

[What you will be working on]

Data architecture and modelling

Design and develop the conceptual, logical and physical data models for investment data domains: security and reference data, positions and holdings, transactions, valuations and pricing, performance, benchmarks, risk, compliance, accounting and client reporting. Define and govern golden‑source, reference‑data and master‑data standards for instruments, parties, legal entities, accounts and portfolios, including identifier strategy, entity resolution, lifecycle states, schema versioning and change control. Produce implementation‑ready artefacts: DDL, transformation and mapping specifications, interface contracts and data quality rules. Define the IBOR / ABOR data design and the reconciliation points between investment and accounting books of record, with end‑to‑end auditability. Define source‑to‑target data flows and mappings centred on SimCorp Dimension, covering market and reference data vendors, custodians, counterparties, and downstream reporting and analytics.

Unstructured data and retrieval architecture

Develop the conceptual, logical and physical data models for unstructured investment‑information domains, including sell‑side and broker research, market commentary, news, internal analytics and knowledge assets. Define the architecture and standards for document ingestion, processing and indexing, ensuring that source context, document structure and temporal attributes are preserved. Define metadata, taxonomy and classification standards for unstructured content to support governance, discoverability and retrieval. Define the architecture and technical standards for the vector database and embedding lifecycle, including embedding model selection, index design, evaluation and retrieval optimisation, and guide the AI Engineer’s implementation. Define the entity and metadata mappings that link unstructured content to portfolio holdings and trades, enabling relevant research and news to be retrieved against current exposures. Design hybrid retrieval orchestration spanning all three layers (vector similarity combined with keyword filtering and metadata ranking) to support near‑real‑time market surveillance.

Platform and integration

Define integration patterns and interface standards for batch, API, messaging and event‑driven exchange across the investment‑data ecosystem. Shape the target data platform for analytics and reporting across on‑premise RDBMS / data warehouse and cloud lakehouse, and guide the migration path between them. Evaluate technology and vendor options, weighing performance, resiliency, security, cost and operational complexity. Take part in platform upgrades, replatforming and data migration, owning data mapping, parallel runs, cutover, reconciliation and legacy decommissioning.

Governance, quality and control

Develop the data‑governance framework and accountable ownership model covering metadata, business and technical lineage, data quality, retention, and access rights. Ensure designs meet MAS technology standards and Whole of Government compliance requirements. Define data classification and access controls for sensitive investment data, applied consistently across all three layers including unstructured research content. Define source‑data lineage and audit requirements for reconciliation outcomes and AI retrieval; work with the AI Engineer to ensure end‑to‑end traceability across data, retrieval, model and application logs.

Leadership and delivery

Serve as the design authority for investment‑data architecture, lead data design reviews, and steer change proposals through the MAS Architecture Review Committee to validate alignment with Enterprise Architecture standards. Partner with portfolio management, investment operations, risk, finance, compliance and vendor teams to translate business needs into data designs. Work to a defined cadence with the Investment Operations & Data Domain Expert to validate the holdings model, reconciliation logic and document classification against confirmed user stories, and with the AI Engineer to assess data‑layer readiness for agent retrieval. Hold decision authority for investment‑data architecture. Make cross‑cutting architecture and technical trade‑offs jointly with the AI Engineer, subject to validation by the Domain Expert that the proposed design meets operational needs. Translate complex data concepts for senior business and technology stakeholders. Ensure that the data architecture provides governed metadata, semantic definitions and access controls for analytics, machine learning and generative AI use cases.

[What we are looking for]

Must have:

• Degree in Computer Science, Information Systems, Engineering or a related field, or equivalent experience.


• At least 10 years of relevant experience across data architecture, data modelling or senior data engineering, with substantial experience designing data solutions for investment management, asset management, institutional investment, capital markets or closely related financial‑services domains.


• Deep knowledge of investment data domains including security/reference data, positions and holdings, transactions, valuations/pricing, performance, benchmarks and IBOR/ABOR.


• Experience designing and implementing reconciliations across IBOR, ABOR, custodians, market data providers and downstream reporting systems.


• Hands on experience with modern data platforms such as Snowflake, Databricks, Spark, Kafka, or cloud‑native data services on AWS or Azure; containerised deployment (Docker, Kubernetes).


• Strong relational database design experience, preferably Oracle, including performance and high‑availability considerations.


• Track record of delivering at least one large-scale data migration or platform replatforming programme in financial services.


• Working knowledge of data governance, lineage, metadata management and data quality frameworks.


• Strong hands‑on experience in conceptual, logical and physical data modelling, including relational and dimensional modelling, and in producing implementation‑ready data specifications.


• Hands‑on experience with vector search and embedding‑based retrieval — including vector index design, embedding model selection, and hybrid search combining vector similarity with keyword and metadata filtering.


• Strong written and verbal communication, including producing clear architecture documentation.

Preferred:

• Hands‑on experience with front‑to‑back investment management platforms, including their data models and data management capabilities.


• Hands‑on experience with document parsing and OCR for scanned and text‑native PDFs, including layout and table extraction, and with semantic chunking strategies suited to long‑form research documents.


• Experience with market and reference data vendors such as Bloomberg, Refinitiv (LSEG) or ICE, and with economic indicator providers such as Macrobond.


• Knowledge of private markets data, ESG data, or multi‑asset portfolios.


• Familiarity with data mesh, data products and federated governance concepts.


• Experience designing data foundations for retrieval‑augmented generation or similar AI retrieval patterns in a regulated environment.


• Programming experience in SQL, Python or Java.

Personal attributes:

• Balances architectural standards with pragmatic delivery.


• Comfortable owning design decisions and defending them with evidence.


• Builds trust with business users and engineers alike.


• Develops others and raises the team’s design quality.


• Comfortable designing under a human‑in‑the‑loop operating principle, where every automated output must remain reviewable and auditable.

You will be working in a fast‑paced environment that would require the ability to manage multiple priorities and needs of stakeholders, as well as the agility to respond to changes and developments.

As part of the shortlisting process for this role, you may be required to complete a medical declaration and/or undergo further assessment.

This is a 4‑year contract.

All applicants will be notified on whether they are shortlisted or not within 4 weeks of the closing date of this job posting.